How does artificial intelligence improve our daily lives?
There is no doubt that 2023 will be the year when advances in artificial intelligence reshape manufacturing, retail, finance, marketing, media and many other mature industries.
#Artificial intelligence will continue to interest and even perplex people—who don’t yet know how to exploit it. However, innovation driven by technological progress and specific applications in a wide range of industries has become an irreversible trend and often achieves good results. We should be open to generative AI, as developments in the coming months and years may bring advancements to society as a whole.
Generative AI gets creative...
The fascinating thing about generative AI is that it does not analyze existing data, but based on previous work Create new original content using machine learning algorithms trained in. Developers have used it to generate images or text, write code, draw and illustrate, and even create video and sound. Of course, it's not perfect and often produces varying results. Over the next few years, though, generative AI will develop human-like content creation capabilities to facilitate digital content creation. This means that entire infrastructures and ecosystems based on generative AI may emerge to facilitate access to models and services to people without technical expertise. It also empowers everyone to become more efficient and productive.
Artificial intelligence also has a sixth sense...
Just as humans have developed multiple senses to explore, enjoy and survive the world, artificial intelligence is also able to learn from different sources to achieve evolution. Multimodal pre-trained models combine different types of data, including images, text, speech, and numerical data, to understand the world. In this way, one can enter the field of artificial intelligence. Unlike previous single modules, these AI models will devour many different types of data and process them simultaneously, bringing new levels of speed and accuracy to many applications. This can have a positive impact on organizations when it comes to processing and understanding data, sharing information, enhancing internal operations, and even streamlining and optimizing the customer experience.
Today’s artificial intelligence is about improving productivity
Multi-modal pre-trained models outperform single-modal models in understanding, extracting, generating and answering questions. By giving enterprises access to advanced models and data analytics, multi-modal pre-trained models can help increase enterprise productivity, agility and efficiency in today's digital economy.
Artificial intelligence helps to better understand the cloud
Today, more and more enterprises are moving to the cloud. However, the cloud is becoming a patchwork of integrated services that are now increasingly difficult to separate. It can be difficult to navigate, and even cloud experts can only absorb and retain so much information at a time. As recruiting capable IT staff becomes more challenging and cloud talent is in short supply, it's understandable why more cloud service providers are using AI technology to reduce complexity and manage cloud deployments more efficiently.
Artificial Intelligence Enhanced Digital Imaging
It’s fascinating to think that something as small as a smartphone can take great photos and videos. This capability is attributed to computational imaging. The emergence of computational imaging will further change the way humans and machines perceive the world. Using technologies like artificial intelligence and signal processing, digital imaging enables smartphones to work like professional-grade cameras. Digital imaging is revolutionizing digital imaging by making this high-tech tool available to everyone, for example by enhancing portrait lighting to reduce vibration.
Artificial intelligence is ready to take off
In order to improve the processing efficiency of artificial intelligence, a new computer architecture called "memory processing" has emerged. Traditional computer system architecture uses separate processors and memory units to perform data processing tasks. This requires constant transfer of data back and forth between the processor and main memory. In-memory processing overcomes data transfers by bringing processing directly to the location where the data is stored, thereby reducing power consumption and improving system performance. This kind of in-memory processing will become a critical and pervasive computing architecture in the era of artificial intelligence. Developers have been building in-memory computing chips to power a wide range of artificial intelligence applications, from virtual and augmented reality to astronomical data calculations.
Of course, there is much debate about the best applications for AI. But as we have found with many emerging technologies in the past, I believe generative AI will also find a place to coexist with other mature technologies. As it matures, it will complement the skills of the human workforce, helping us be more efficient and creative in the workplace or at home.
The above is the detailed content of How does artificial intelligence improve our daily lives?. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
A new era of VSCode front-end development: 12 highly recommended AI code assistants
Jun 11, 2024 pm 07:47 PM
In the world of front-end development, VSCode has become the tool of choice for countless developers with its powerful functions and rich plug-in ecosystem. In recent years, with the rapid development of artificial intelligence technology, AI code assistants on VSCode have sprung up, greatly improving developers' coding efficiency. AI code assistants on VSCode have sprung up like mushrooms after a rain, greatly improving developers' coding efficiency. It uses artificial intelligence technology to intelligently analyze code and provide precise code completion, automatic error correction, grammar checking and other functions, which greatly reduces developers' errors and tedious manual work during the coding process. Today, I will recommend 12 VSCode front-end development AI code assistants to help you in your programming journey.
